For each word w, and for each sense S of w in a given dictionary D ∈ {WORDNET, ODE}, we construct a sense description d D (S) as a bag of words: where: • def D (S) is the set of words in the textual definition of S (excluding usage examples), automatically lemmatized and partof-speech tagged with the RASP statistical parser (Briscoe and Carroll, 2002); • hyper D (S) is the set of direct hypernyms of S in the taxonomy hierarchy of D (∅ if hypernymy is not available); • domains D (S) includes the set of domain labels possibly assigned to sense S (∅ when no domain is assigned).

in the case of WordNet, we generate def WN (S) from the gloss of S, hyper WN (S) from the noun and verb taxonomy, and domains WN (S) from the subject field codes, i.e.


To make it clear that the performance improvement is not only due to polysemy reduction, we considered a subset of the Senseval-3 test set including only the incorrect answers given by the fine-grained version of Gambl (623 items).

on this data set Gambl performs with 0% accuracy.


In such cases, the system enters an expansion phase, where instances are expanded as follows: Web expansion: New instances of the patterns in P are retrieved from the Web, using the Google search engine.

for each instance {x, y}∈ I, the system creates a set of queries, using each pattern in P instantiated with y.


Traditional classifier learning approaches apply the flat learning strategy.

they equally treat training examples in different classes independently and ignore the commonality among related classes.


5 ) ( x w ⋅ denotes the dot product of the weight vector n R w ∈ and a given instance The well-known perceptron algorithm, as shown in Figure 1, belongs to online learning of linear classifiers, where the learning algorithm represents its t -th hyposthesis by a weight vector received at trial t .

the perceptron algorithm updates the hypothesis by adding a scalar multiple of the instance, as shown in Equation 1 of Figure 1, when there is an error.


This is done by initializing the weight vector in training the linear discriminative function for the lowerlevel class as that of the upper-level class.

the lower-level discriminative function has the preference toward the discriminative function of its upper-level class.


Table 2, we see that both LP Cosine and LP JS achieve higher Recall than SVM.

with small labeled dataset (percentage of labeled data ≤ 25%), the performance improvement by LP is significant.


On the other hand these different structures are not independent from each other and even if they are not structures on the same objects (for instance the semantic units do not correspond one to one to the syntactic units, that is the words) there are links between the different objects of these structures.

considering separately the different simple structures of the sentence does not take into account the whole structure of the sentence, because we lost the interrelation between structures of different levels.


However, the proposed solution is adhoc and cannot be taken seriously as a concept of modularization.

the suggestion for an agreement on the feature space undermines the essence of modular design.


Proposition 3 PSS merge is commutative: for any two PSSs, S 1 , S 2 , S 1 ⋒S 2 = S 2 ⋒S 1 .

either both are defined or both are undefined.


But if the verb in question subcategorizes for a dative or an ablative oblique object, then this object will be transformed into a direct object in accusative case after causativization (Example 16).

the causativation will select the case of the object of the causative verb, so as not to "interfere" with the object of the verb that is causativized.


This extra material renders the entire utterance ungrammatical-the string would not be generated by a correct grammar of fluent English.

attractive tools for natural language understanding systems, such as Treebank grammars for written corpora, naturally lack appropriate rules for analyzing these constructions.


Well-formedness rule for repairs (WFR) A repair α γ is well-formed if and only if there is a string β such that the string αβ and * γ is well-formed, where β is a completion of the constituent directly dominating the last element of α.

(and is to be deleted if that last element is itself a sentence connective) the string α is a prefix of a phrase whose completion, β-if it were present-would render the whole phrase αβ grammatically conjoinable with the repair γ.


This kind of conjoinability typically requires the syntactic categories of the conjuncts to be the same (Chomsky, 1957, 36).

a rule schema such as (2) where X is a syntactic category, is preferred over one where X is not constrained to be the same on either side of the conjunction.


A dependency is a modification relation in which a dependent bunsetsu depends on a head bunsetsu.

the dependent bunsetsu and the head bunsetsu work as modifier and modifyee, respectively.


Furthermore, the program can specify the positions and types of clause boundaries simply from a local morphological analysis.

for a sentence morphologically analyzed by ChaSen (Matsumoto et al., 1999), the positions of clause boundaries are identified and clause boundary labels are inserted there.


Our method parses by giving consideration to the dependency structures in each clause boundary unit, which were previously parsed.

the method does not consider all bunsetsus located on the right-hand side as candidates for a head bunsetsu but calculates only dependency relations within each clause boundary unit that do not cross any other relation in previously parsed dependency structures.


We view this problem as a first step in predicting the system state of a dialog manager and in predicting the system utterance during an incremental execution of a dialog.

we learn models for predicting dialog acts of utterances, and models for predicting subtask structures of dialogs.


It is well known that Kullback-Leibler divergence (Cover and Thomas 1991) is positive and increases as the overlap between the two distributions decreases.

maximizing Kullback-Leibler divergence implies that the overlap between two distributions is minimized.


However, the space requirements of the two training methods are the same.

even though the covariance matrix has size £ 3 e ( , there is never any need to store the entire matrix in memory.


The blue line in Figure 2 is the result of the supervised CRF algorithm, trained only on the labeled set .

by using the supervised CRF model, the system predicted 3334 out of 7472 gene mentions, of which 2435 were correct, resulting in a precision of 0.73, recall of 0.33 and F-measure of 0.45.


Our proposed framework is derived from an error minimization approach that provides a simple solution for directly optimizing any evaluation measure.

focusing on sequential segmentation tasks, i.e.


In the same spirit as the above studies, we first propose a generalization framework for CRF training that allows us to optimize directly not only the error rate, but also any evaluation measure.

our framework can incorporate any evaluation measure of interest into the loss function and then optimize this loss function as the training objective function.


Z λ (x) = ỹ∈Y c∈C(ỹ,x) Φ c (ỹ, x; λ) is a normalization factor over all output values, Y.

following the definitions of (Sha and Pereira, 2003), a log-linear combination of weighted features, Φ c (y, x; λ) = exp(λ • f c (y, x)), is used as individual potential functions, where f c represents a feature vector obtained from the corresponding clique c. is the CRf's global feature vector for x and y.


By using d(), the minimization of the error rate can be rewritten as the minimization of the sum of 0-1 (step) losses of the given training data.

arg min λ L λ where δ(r) is a step function returning 0 if r<0 and 1 otherwise.


That is, arg min λ L λ where δ(r) is a step function returning 0 if r<0 and 1 otherwise.

δ is 0 if the value of the discriminant function of the correct output g(y * k , x k , λ) is greater than that of the maximum incorrect output g(y k , x k , λ), and δ is 1 otherwise.


Therefore, ML/MAP can be seen as one special case of the framework proposed here.

our method provides a generalized framework of CRF training.


7, we only need to evaluate the segments corresponding to the maximum incorrect outputỹ to calculate F P l .

s j ∈ S(x k )\s * k can be reduced to s j ∈s k , wheres k represents segments corresponding to the maximum incorrect outputỹ.


These early steps coincide with the boosting steps most of the time.

the effect of backward steps is more visible at later stages.


Examining the scores for essay C, for example, we see that e-rater gave a perfect score of 6, while Jess gave only a score of 5 after converting to e-rater's 6-point system.

the length of the essay could not compensate for various weak points in the essay under Jess's point-deduction system.


We also found that neither the expert raters nor Jess, had much correlation with LUT, which shows that LUT does not reflect features indicating writing ability.

lUT measures quite different laterals from writing ability.


Thus, it is favorable for error detection systems aiming at Japanese learners to be capable of detecting these errors.

such systems need to somehow distinguish mass and count nouns.


In this paper, we show that a noisy channel model instantiated within the paradigm of Statistical Machine Translation (SMT) (Brown et al., 1993) can successfully provide editorial assistance for non-native writers.

the SMT approach provides a natural mechanism for suggesting a correction, rather than simply stranding the user with a flag indicating that the text contains an error.


Although we anticipate that it will take a significant lead time to assemble the necessary aligned data, once a sufficiently large corpus is in hand, we expect to begin exploring ways to improve our SMT system by tailoring it more specifically to the demands of editorial assistance.

we expect to be looking into alternative word alignment models and possibly enhancing our system's decoder using some of the richer, more structured language models that are beginning to emerge.


Besides this type of information, people often have other kinds of knowledge concerning the target word.

they know how the latter relates to other words.


Given the diversity of situations in which a given object may occur we are likely to build many associations.

lexical graphs tend to become complex, too complex to be a good representation to support navigation.


Although this kind of supertag describes the role of each word in a sentence, it still does not specify the entire local context; for instance, it associates the information that a word functions as a subject only with the subject and not with the verb that takes the subject.

it does not predict the relations under a given word.


Second, and more importantly, it can be seen as a method for query expansion in a non-ad-hoc manner.

bAYESUM is a statistically justified query expansion method in the language modeling for IR framework (Ponte and Croft, 1998).


Supposing there are J total queries and K total documents, we say that the nth word from the sth sentence in document d, w dsn , has a corresponding hidden variable, z dsn that specifies exactly which of these distributions is used to generate that one word.

z dsn is a vector of length 1 + J + K, where exactly one element is 1 and the rest are 0.


The second true system, KL+REL is based on augmenting the KL system with blind relevance feedback (query expansion).

we first run each query against the document set returned by the relevance judgments and retrieve the top n sentences.


The pertinence of pattern i P to a word pair j j Y X : is the expected relational similarity between a word pair k k Y X : , randomly selected from W according to the probability distribution ) : , and the word pair can be interpreted as the degree to which the pair k k Y X : is representative (i.e., typical) of pairs that fit the pattern i P .

i P is pertinent to j j Y X : if highly typical word pairs k k Y X : for the pattern i P tend to be relationally similar to j j Y X : .


By "TF = f, IDF = n / 1 ", for example (row 8), we mean that f plays a role that is analogous to term frequency and n / 1 plays a role that is analogous to inverse document frequency.

in row 8, the patterns are ranked in decreasing order of pattern frequency divided by pair frequency.


We implemented this algorithm as a perl script, with strong optimizations of data structures so as to reduce memory and time usage.

form-level structures are shared between sentences.


On the EASy corpus, results are also relevant, but sometimes more difficult to interpret, because of the relative small size of the corpus and because of its heterogeneity.

it contains email and oral transcriptions sub-corpora that introduce a lot of noise.


we show that the reranking parser described in Charniak and Johnson (2005) improves performance of the parser on Brown to 85.2%.


(Though we also present results with indomain data as a reference point.)

we note the effects of two comparatively recent techniques for parser improvement.


the results for 16 classes with K = 53) this never happens in the case where the correspondence between the number of gold standard classes and the values of K is the closest.

the informative values of K prove really informative for IB.


The elements of the vectors given by tf•idf are the co-occurrence frequencies of words and contexts, weighted by normalized idf.

denoting the number of distinct words and contexts as N and M , respectively, (1) where tf(w i , c j ) is the co-occurrence frequency of word w i and context c j .


For terms u and v, Goemans and Williamson (1995) approximate the angular similarity by where θ( u, u) is the angle between u and u.

the angular similarity gives the cosine by the probability can be derived from the Hamming distance: By combining equations 5 and 6 we get the following approximation of the cosine distance: the cosine of two context vectors is approximated by the cosine of the Hamming distance between their two signatures normalised by the size of the signatures.


Looking more closely, we conclude that compared to event terms, named entities are more representative of the documents in which they are included.

event terms are more likely to be distributed around all the document sets, whereas named entities are more topic-specific and therefore cluster in a particular document set more.


First, we provide a novel comparison of supervised and weakly supervised approaches.

we study how constituent-based and word-based methods port to different domains and show that the latter tend to be more robust.


This paper describes one part of an exploration into how this information can be captured automatically.

we have developed annotation guidelines to minimize discrepant judgments and annotated 58 articles, comprising 2288 events; we have developed a method for measuring inter-annotator agreement when the judgments are intervals on a scale; and we have shown that machine learning techniques applied to the annotated data considerably out-perform a baseline and approach human performance.


However, some probability mass is lost this way: there is a nonzero PCFG probability (namely, P PCFG (t 1 )) that the structures match.

we may have identical subtrees in two different ways: either due to a copy operation, or due to a PCFG derivation.


Because the Within model is finer grained than the Between model, it can be used to capture the parallelism effect in coordination.

this model could explain parallelism in coordination as an instance of a more general priming effect.


The results show that the approach taken here can be successfully applied to the modeling of experimental data.

both the Within and the Copy models show statistically reliable parallelism effects.


A further contribution of the present paper has been to develop a methodology for analyzing the (re-)use of syntactic rules over time in a corpus.

we have defined an algorithm for randomizing the constituents of a treebank, yielding a baseline estimate of chance repetition.


But compared to all other works, our classifier stacking model gave better or equal results for all three measures.

the classifier ensemble model and SVM model of our parser achieved second and third highest LP, LR and F1 for sentences ≤ 100 words as shown in Table 3.


Their algorithm is therefore the labelled recall algorithm of Goodman (1996) but applied to rules.

it returns the tree whose expected number of correct rules is maximal.


The use of WSD has produced successful experiments for query expansion techniques.

some attempts exploited WordNet to enrich queries with semantically-related terms.


Then, we exploit such model to match the contexts of the target word, providing a decision criterion for sense matching.

we expect that under a matching sense the target word would occur in prototypical contexts of the source word.


For example, "or" nodes are packed when they have same end positions and same labels in the first order semi-CRFs, In general, we can pack different "or" nodes that yield equivalent feature functions in the following nodes.

"or" nodes are packed when the following states use partial information on the preceding states.


Such a problem -where the classification consists of several subtasks or attributes -presents additional challenges, when compared to a standard sequence classification task.

there are inter-dependencies between the subtasks that need to be modeled explicitly; predicting the tags independently of each other will likely result in inconsistent classifications.


We apply the SGT smoothing method to our HMM IE systems to alleviate the data sparseness problem in HMM training.

the emission probability distribution for each state is smoothed using the SGT method.


Step 1: Identify from the entire documents the text segments that are relevant to a specific slot extraction.

the document is filtered by locating text segments that might contain a filler.


We propose a segment retrieval approach for performing the first subtask by also using HMMs.

it trains an HMM from labelled segments in texts, and then use the learned HMM to determine whether a segment is relevant or not with regard to a specific extraction task.


Then content-based features are employed for candidate verification.

hTML tag similarities have been exploited to verify parallelism between pages.


So we find that the feature selection scheme and the term weighting scheme are related to the classifier, which is worth noting.

no feature selection scheme or term weighting scheme is absolutely the best for all classifiers.


Experiments show the effectiveness of our approach, providing a low cost solution for the Cross Language Text Categorization task.

when bilingual dictionaries are available the performance of the categorization gets close to that of monolingual text categorization.


Experiments show the effectiveness of our approach, providing a simple and low cost solution for the Cross-Language Text Categorization task.

when bilingual dictionaries/repositories are available, the performance of the categorization gets close to that of monolingual TC.


In this paper we have shown that the problem of cross-language text categorization on comparable corpora is a feasible task.

it is possible to deal with it even when no bilingual resources are available.


The gain for a feature needs to be re-computed only when the feature reaches the top of a priority queue ordered by gain.

this happens when the feature is the top candidate for inclusion in the model.


Since the gains need to be positive to proceed the process, the difference between the Likelihood of the current and the ML value becomes smaller and smaller.

the possible gain each feature may add to the model gets smaller.


Then, the SGC algorithm is performed on each of the feature groups and new features are selected from each of them.

the feature space splitting and subspace merging are performed mainly on the feature-to-instance mapping tables.


Since the majority of the corpus is labeled via automatic methods, the fscores for the prosodic labels are not high.

4 and p have f-scores of about 70% and 60% respectively (Wong et al., 2005).


CME is significantly smaller than the reported results using the boosting method.

using CME instead of boosting incurs a performance hit.


The difference between our best detector and the gold edits in parsing (1.51%) is smaller than the difference between the TAG-based detector and the gold edits (1.9%).

if we use the gold edits as the upper bound, we see a relative error reduction of 20.5%.


For sake of simplicity, in this research we only consider transfer rules that are used for transferring the source language to the target language and keep other kinds of rules untouched.

we only consider the influence of transfer rules on translation quality 1 .


Logic suggests that if a system identifies sentences rather than documents, it should use a corpus weighting scheme based on the number of sentences rather than the number documents.

the system should replace IDF with the Inverse Sentence Frequency (ISF), where N in (1) is the total number of sentences and n i is the number of sentences with term i.


", it means that my father entered the bag, which is totally different from the original meaning.

since the morphological analysis is the first-step in most NLP applications, the sentences with incorrect word spacing must be corrected for their further processing.


The proposed method automatically adapts the window size Ò.

this method begins with a bigram model, and it shrinks to an unigram model when data sparseness occurs.


The main drawback of Ò-gram model is that it suffers from data sparseness however large the corpus is.

there are many Ò-grams of which frequency is zero.


grams only when higher performance is expected.

,´Ò (2) ´Ò Ò • ½µ that is larger than a predefined threshold EXP implies that È´Ý Ü´Ò •½µ µ is different from È´Ý Ü Ò µ.


The difference ´Ò Ò ½µ between Ò-grams and´Ò ½µ-grams is, once again, measured by Kullback-Leibler divergence.

´Ò Ò ½µ Ã ÄÈ´Ý Ü Ò µ È´Ý Ü´Ò ½µ µ If ´Ò Ò ½µ is smaller than another predefined threshold SHR , then´Ò ½µ-grams are used instead of Ò-grams.


The number of errors made by expanding first is 108,831 while that by shrinking first is 114,343.

if shrinking is applied ahead of expanding, 5,512 additional errors are made.


In order to reflect the idea of bidirectional context in the proposed model, the model is enhanced by modifying È´Û ½ Û Ý µ in Equation (1).

the likelihood of È´Û ½ Û Ý µ is expanded to Since the coefficients of Equation (4) were determined arbitrarily (Kang, 2004), they are replaced with parameters « of which values are determined using a held-out data.


When only the right context is used, the accuracy gets 88.26% which is worse than the left context only.

the original Ò-gram is a relatively good model.


Since the available corpus size is rather small we could not compare the results obtained with various other word alignment algorithms proposed in the literature.

we like to compare the proposed scheme with the famous IBM models.


By using a document retrieval method, we efficiently identify Katakana words that are phonetically similar to a candidate loanword.

we use a candidate loanword, and each Katakana word as a query and a document, respectively.


In practice, we found the average number of emissions per sentence in our corpus (where each symbol is counted as the number of its predecessor emissions) to be 455, where the average number of words per sentence is about 18.

the cost of operating over an ambiguous sentence representation increases the size of the sentence (from 18 to 455), but on the other hand, it reduces the probabilistic model by a factor of 10 (as discussed above).


Formulating an explicit probabilistic model permits us to cleanly separate assumptions about the input and properties of likely segmentations from details of algorithms used to find such solutions.

this paper demonstrates the importance of contextual dependencies for word segmentation by comparing two probabilistic models that differ only in that the first assumes that the probability of a word is independent of its local context, while the second incorporates bigram dependencies between adjacent words.


Any other solution will match the empirical distribution of the data less well.

a solution with additional word boundaries must have 1 − p $ > 0, which means it wastes probability mass modeling unseen data (which can now be generated by concatenating observed utterances together).


However, the mathematical statement of the model and the approximations necessary for the search procedure make it unclear how to modify the model in any interesting way.

the fourth step uses a uniform distribution, which creates a unigram constraint that cannot easily be changed.


1 A final detail of our model is the distribution on utterance lengths, which is geometric.

we assume a grammar similar to the one shown in Figure 1, with the addition of a symmetric Beta( τ 2 ) prior over the probability of the U productions, 2 and the substitution of the DP for the standard multinomial distribution over the W productions.


This result occurs because our system often mis-analyzes frequently occurring words.

many of these words occur in common collocations such as what's that and do you, which the system interprets as a single words.


From these figures, we can see that the MBDP model assigns higher probability to the solution found by our Gibbs sampler than to the solution found by Brent's own incremental search algorithm.

brent's model does prefer the lower-accuracy collocation solution, but his search algorithm instead finds a higher-accuracy but lower-probability solution.


In our case, we define a bigram model by assuming each word has a different distribution over the words that follow it, but all these distributions are linked.

the definition of our bigram language model as an HDP is P (w i |w i−1 = w) is distributed according to H w , a DP specific to word w. H w is linked to the DPs for all other words by the fact that they share a common base distribution G, which is generated from another DP.


jectives, which has led to misleading results.

previous work suggested that the use of word-to-word dependencies has little effect on word segmentation.


Since SVMs can handle large feature sets, we utilize additional morphological features.

we found the combination of the Number and the Construct features to be most effective in improving chunking results.


A further contribution of the paper is to also use multi-tagging for the POS tags, and to maintain some POS ambiguity in the language processing pipeline.

since POS tags are important features for the supertagger, we investigate how supertagging accuracy can be improved by not prematurely committing to a POS tag decision.


In future work we will investigate maintaining tag ambiguity further down the language processing pipeline and exploiting the uncertainty from previous stages.

we will incorporate real-valued POS tag and lexical category features in the statistical parsing model.


The advantage of the reordering model is that it can better model global phrase reordering using a four-valued reordering pattern, and it can be easily The problem of the global phrase reordering model is the cost of parameter estimation.

the N-best phrase alignment described in Section 4.1 is computationally expensive.


We first discuss several challenges to error-driven discriminative approaches.

we explore different ways of updating parameters given a training example.


We then present a novel way to parameterize and introduce learning into the initial phrase extraction process.

we introduce alignment constellation features, which allow us to weight phrases based on the word alignment pattern that led to their extraction.


(2000), Wermter and Hahn (2004)), however, have already observed that ranking the candidates merely by their frequency of occurrence fares quite well compared with various more sophisticated association measures (AMs such as t-test, loglikelihood, etc.).

the precision/recall value comparison between the various AMs exhibits a rather inconclusive picture in  and  as to whether sophisticated statistical AMs are actually more viable than frequency counting.


In this work, we make two independent observations about Named Entities encountered in such corpora, and use them to develop an algorithm that extracts pairs of NEs across languages.

given a bilingual corpora that is weakly temporally aligned, and a capability to annotate the text in one of the languages with NEs, our algorithm identifies the corresponding NEs in the second language text, and annotates them with the appropriate type, as in the source text.


(2) Path-enclosed Tree (PT): the smallest common sub-tree including the two entities.

the sub-tree is enclosed by the shortest path linking the two entities in the parse tree (this path is also commonly-used as the path tree feature in the feature-based methods).


This may be due to two reasons: 1) the dependency tree (Culotta and Sorensen, 2004) and the shortest path (Bunescu and Mooney, 2005) lack the internal hierarchical phrase structure information, so their corresponding kernels can only carry out node-matching directly over the nodes with word tokens; 2) the parse tree kernel has less constraints.

it is 4 Bunescu and Mooney (2005) used the ACE 2002 corpus, including 422 documents, which is known to have many inconsistencies than the 2003 version.


For example, the sentence "John McEnroe won the match..." contributes to probability estimation of the class-based selectional preference win(x) → human(x) (since John McEnroe is a human).

contexts, it contributes also to the induction of the entailment relation between win and play, as John McEnroe has the property of playing.


Given a pair v t and v h we define the following entailment strength indicator S(v t , v h ).

the measure S nom (v t , v h ) is derived from point-wise mutual information (Church and Hanks, 1989): where nom is the event of having a nominalized textual entailment pattern and pers is the event of having an agentive nominalization of verbs.


Even if, as discussed in (Chklovski and Pantel, 2004), these patterns are not specifically defined as entailment detectors, they can be useful for this purpose.

some of these patterns can be used to investigate the backwardpresupposition entailment.


3, the entailment relation is a semantic relation defined at the synset level, standing in the verb sub-hierarchy.

each pair of synsets (S t , S h ) is an oriented entailment relation between S t and S h .


The best pattern or combined method is the one that is able to most neatly split entailment pairs from random pairs.

it should in average assign higher S values to pairs in the True Set.


A possible drawback of U-DOP, however, is the statistical inconsistency of its estimator (Johnson 2002) which is inherited from the DOP1 model (Bod 1998).

even with unlimited training data, U-DOP's estimator is not guaranteed to converge to the correct weight distribution.


Together with the high computational cost of reestimation we propose even more drastic pruning than we did in Bod (2006) for U-DOP.

while for sentences ≤ 7 words we use all binary trees, for each sentence ≥ 8 words we randomly sample a fixed number of 128 trees (which effectively favors more frequent trees).


This has roughly the same effect as the correction factor used in Bod (2003Bod ( , 2006.

for each particular depth we sample subtrees by first randomly selecting a node in a random tree from the binary tree set after which we select random expansions from that node until a subtree of the particular depth is obtained.


Instead, we imposed a global threshold on exploration of the search space.

if the parser has found some complete parse and has explored at least 100K states (i.e.


This process is guaranteed to converge to a local extremum of the data likelihood, but initial production probability estimates greatly influence the final grammar (Carroll and Charniak, 1992).

uniform initial estimates are an (unstable) fixed point.


Of course, the parses' symbols have nothing to anchor them to our intended treebank symbols.

an NP in one of these grammars may correspond to the target symbol VP, or may not correspond well to any target symbol.


For efficiency, only hypernym, hyponym and entailment relations are considered and search depth is set to 2 in our experiments.

the semantic variations are not considered for NE heads and modifiers.


Let P π (u) denote the probability that a production π of the MRL grammar covers the NL substring u.

the NL substring u expresses the semantic concept of a production π with probability P π (u).


What makes the Jibiki platform quite unique is the fact that it provides these services regardless of the dictionary structure.

it may be used by any dictionary builder to give access and collaboratively edit any dictionary, provided that the resulting dictionary will be freely accessible online.


This recursive calculation yields a microaveraged value that represents the reachability of segments via downward edge traversals from a given node in the hierarchy.

it gives a kind of weighted value for the number of segments described by a given keyword phrase or its narrower-term keyword phrases.


They pose important problems for NLP applications, both text analysis and text production perspectives being concerned.

collocations 1 are highly problematic, for at least two reasons: first, because their linguistic status and properties are unclear (as pointed out by McKeown and Radev (2000), their definition is rather vague, and the distinction from other types of expressions is not clearly drawn); second, because they are prevalent in language.


The agreement statistics on the annotations for each method are shown in Table 3  For reporting n-best precision results, we used as reference set the annotated pairs on which at least two of the three annotators agreed.

from the 500 initial pairs retrieved with each method, 497 pairs were retained in the first case (parse method), and 483 pairs in the second (window method).


Rules that satisfy this property are said to be induced by G. 2 For example, rule (d) in Table 1 is valid according to GHKM, since the spans corresponding to 1 Note that we use a slightly different terminology.

2 an xRs rule ri is extracted from G by taking a subtree γ ∈ π as lhs(ri), appending a variable to each leaf node of γ that is internal to π, adding those variables to rhs(ri), ordering them in accordance to a, and if necessary inserting any word of f to ensure that rhs(ri) is a sequence of contiguous spans (e.g., [4][5][6] [7][8] for rule (f) in Table 1).


These two effects together produce a power-law distribution where many unique words are observed, most of them rarely.

for a vocabulary of unbounded size and for d > 0, the number of unique words scales as O(θT d ) where T is the total number of words.


It can be shown that the states of variables will converge to the required samples from the posterior distribution after a sufficient number of iterations.

for the hierarchical Pitman-Yor language model, the variables consist of, for each u and each word x ul drawn from G u , the index k ul of the draw from G π(u) assigned x ul .


It is revealed that 99.2 percent of chat terms in NIL corpus fall into the first four phonetic mapping types that make use of phonetic mappings.

99.2 percent of chat terms can be represented by phonetic mappings.


Further study on named entity clustering shows that the five measures are quite useful.

cODc measure is very stable on wordword and name-name experiments.


We can define NE i and NE j have an indirect association if a ij is larger than a threshold λ.

nE i and nE j should associate with at least λ common named entities directly.


We can define NE i and NE j have an indirect association if s ij is larger than a threshold δ.

if s ij >δ, then the link detection test says "yes", otherwise it says "no".


If score(NE j , c ) is larger than a threshold, then NE j is placed into cluster c .

nE j denotes the same person as those in c .


Further study on named entity clustering shows that the five measures -say, VariantDice, VariantOverlap, ariantJaccard, VariantCosine and CODC, are quite useful.

cODc is very stable on word-word and namename experiments.


Prior to retrieval, we prepare a semantically annotated textbase by applying NLP tools including deep parsers and term recognizers.

all sentences are annotated in advance with semantic structures and are stored in a structured database.


Speed is indispensable for real-time interactive text retrieval systems, and accuracy is key for the motivation of semantic retrieval.

our motivation for employing semantic retrieval  [sentence] >> ([word arg1="$subject" arg2="$object" rel_type="activation"] & ([phrase id="$subject"] > [entity_name type="gene" gene_id="GHS019685"]) & ([phrase cat="np" id="$object"] > [entity_name type="gene" gene_id="GHS009426"])) Figure 7: Queries of the extended region algebra for Query 4-3 (upper: keyword search, lower: semantic search) was to provide a device for the accurate identification of relational concepts.


That is, our motivation for employing semantic retrieval  [sentence] >> ([word arg1="$subject" arg2="$object" rel_type="activation"] & ([phrase id="$subject"] > [entity_name type="gene" gene_id="GHS019685"]) & ([phrase cat="np" id="$object"] > [entity_name type="gene" gene_id="GHS009426"])) Figure 7: Queries of the extended region algebra for Query 4-3 (upper: keyword search, lower: semantic search) was to provide a device for the accurate identification of relational concepts.

high precision is desired in text retrieval from huge texts because users want to extract relevant information, rather than collect exhaustive information.


While the effectiveness of the test of different perspectives is demonstrated in Figure 1, one may wonder why the distribution divergence of the document collection pair with different perspectives falls in the middle range and what causes the large and small divergence of the document collection pairs with different topics (DT) and the same topic (ST) or perspective (SP), respectively.


We use a word sense disambiguation system that integrates both local and topical features.

we use the current word and its partof-speech, a local context of three words to the left and right of the ambiguous word, the parts-ofspeech of the surrounding words, and a global context implemented through sense-specific keywords determined as a list of at most five words occurring at least three times in the contexts defining a certain word sense.


The experiments reported here pointed out many areas of our system which previous failure analysis of the basic QA system had not pinpointed as being too problematic, but for which improvement should help the Constraints process.

this work brought to light a matter of major significance, term equivalence, which we had not previously focused on too much (and neither had the QA community as a whole).


We have identified several areas where improvement to our system would make the Constraints process more effective, thus getting a double benefit.

we feel that much more attention should be paid to the problem of determining if two entities are the same (or "close enough").


In this paper, we are interested in considering the term dependence to improve the answer reranking for definitional QA.

the language model is utilized to capture the term dependence.


The λ can be learned from a training corpus using an Expectation Maximization (EM) algorithm.

we estimate λ by maximizing the likelihood of all training instances, given the bigram or biterm model: BP and P(t 1 ) are ignored because they do not affect λ .


We use FSs over the signature τ to represent and simulate LIG symbols.

fSs will encode lists in the natural way, hence the features HEAD and TAIL.


We believe that it should be possible to optimize the mapping such that much smaller grammars are generated.

we are looking into mappings of one-reentrant UGs to other MCSL formalisms, notably TAG.


We use an algorithm resembling probabilistic bottom-up parsing to build a WIDL-expression for an input Chinese string: each contiguous span u 7   .

reordering of the component tiles are permitted by the S c operators (assigned non-zero probability), but the longer the movement from the original order of the tiles, the lower the probability.


Unit selection synthesis 1 -a technique which concatenates segments of natural speech selected from a database-has been found to be capable of producing high quality synthetic speech, especially for utterances that are similar to the speech in the database in terms of style, delivery, and coverage (Black and Lenzo, 2001).

in the limited domain of a spoken language dialogue system, it is possible to achieve highly natural synthesis with a purpose-built voice (Black and Lenzo, 2000).


we used a partitioning algorithm of the CLUTO (Karypis, 2002) library for clustering.


Regarding the thresholds of the clustering phase (Section 3.2), after training the thresholds with the collection S1 of 65 news articles we have concluded: • The first step in the clustering phase, 1(a), performs a good first grouping with threshold relatively high; in this case 6 or 7.

documents in different languages that have more cognates in common than 6 or 7 are grouped into the same cluster.


On the other hand, regarding the NE category enforce on match in steps 1(a) and 2(b), we tried with the two NE categories of cognates shared by the most number of documents.

with S2 and S3 corpus the NE categories of the cognates shared by the most number of documents was LOCATION followed by PERSON.


This study aims at identifying when an event written in text occurs.

we classify a sentence for an event into four time-slots; morning, daytime, evening, and night.


In this paper, we propose a method for estimating occurrence time of events expressed in informal text.

we classify sentences in text into one of four time-slots; morning, daytime, evening, and night.


We can figure out that there are much more time-associated words than explicit temporal expressions in blog text.

we can deal with wide coverage of sentences in informal text by our method with time-associated words.


Therefore, we use a semi-supervised method with a small amount of labeled data and a large amount of unlabeled data, because to prepare a large quantity of labeled data is costly, while unlabeled data is easy to obtain.

we adopt the Naïve Bayes classifier backed up with the Expectation Maximization (EM) algorithm (Dempster et al., 1977) for semi-supervised learning.


Annotation projection tackles this problem by leveraging parallel corpora and the high-accuracy tools (e.g., parsers, taggers) available for a few languages.

through the use of word alignments, annotations are transfered from resource-rich languages onto low density ones.


One of the restrictions of TREC QA is that "questions within a series must be processed in order, without looking ahead."

systems are allowed to use answers to earlier questions to help answer later questions in the same series, but can not use later questions to help answer earlier questions.


We use the language model and the local tree order model to create N-best target dependency tree orders.

we generate the N-best lists from a simple log-linear combination of the two models: where o(t) denotes an order of the target.


In our model, the trees do not rule out any alignments, but rather softly influence the probability of transitioning between alignment positions.

transition probabilities condition upon paths through the target parse tree, allowing the model to prefer distortions which respect the tree structure.


The HMM model captures the intuition that the alignment vector a will in general progress across the sentence e in a pattern which is mostly local, perhaps with a few large jumps.

alignments are locally monotonic more often than not.


(2006) shows that thresholding the posterior probabilities of alignments improves AER relative to computing Viterbi alignments.

we choose a threshold τ (typically τ = 0.5), and take a = {(i, j) : p(a j = i|f, e) > τ }.


Given a threshold and a matrix c of combined weights for each possible link in an alignment, we include a link (i, j) only if its weight c ij is above-threshold and it is connected to the maximum weighted link in both row i and column j.

only the maximum in each column and row and a contiguous enclosing span of above-threshold links are included in the alignment.


More importantly, our main focus and contribution is in showing how we can improve the effectiveness of a basic active learning approach when it is used for domain adaptation.

we explore the issue of different sense priors across different domains.


On the other hand, if m = 1 (i.e., the implication does hold), then the first column of the data matrix is generated by choosing values for f 1 independently by π 1 , but the second column is generated differently.

if for a particular language, we have that f 1 is true, then the fact that the implication holds means that f 2 must be true.


In essence, we take the FLAT model and build a notion of language relatedness into it.

we enforce a hierarchy on the m implication variables.


In this section, we perform a quantitative evaluation of the results based on predictive power.

one generally would prefer a system that finds implications that hold with high probability across the data.


The most widely used LM is a probabilistic language model (PLM), which assigns a probability to a sentence or a word sequence.

ngrams with maximum likelihood estimation (nLMs) are often used.


Another problem for DLMs is that the number of features becomes very large, because all possible Ngrams are used as features.

the memory requirement becomes a serious problem because quite a few active sets with many features have to be stored, not only at training time, but also at classification time.


Initial studies into the vowel inventories show that for a randomly chosen vowel, its hamming distance from the other vowels in the same inventory varies with the inventory size.

the error correcting capability of a vowel inventory seems to be dependent on the size of the inventory.


MRRs of the phonetic transliteration In both methods, CoreMRRs were higher than 0.49 for all languages.

if the answer is in the target language texts, then the method finds the correct answer within the top 2 words.


Hindi has a large phoneme inventory compared to Korean, Arabic, and Chinese, so the relationship between English phonemes and Hindi phonemes is relatively regular, and only small number of language specific transliteration rules exist.

the language specific influences from Hindi are smaller than those from other languages.


The earliest bit of Chinese translation theory related to transliteration may be the principle of "Names should follow their bearers, while things should follow Chinese."

names should be transliterated, while things should be translated according to their meanings.


It can be conjectured from the results that semantic transliteration is substantially superior to phonetic transliteration.

knowing the language information improved the overall MRR performance to 0.5952; and with additional gender information, the best performance of 0.6812 was obtained.


Such lexicons can be created manually, or automatically from data.

given a lexicon L and a surface word w, we define the following operations: • Stemming -let S w = {s 1 , ..., s l } be the set of possible morphological stems (lemmas) of w according to L. 2 • Inflection -let I w = {i 1 , ..., i m } be the set of surface form words that have the same stem as w. i ∈ I w iff S i S w = ∅.


The model implemented here is of second order: at any decision point t we condition the probability distribution over labels on the previous two predictions y t−1 and y t−2 in addition to the given (static) word context from both the source and target sentences.

the probability of a predicted inflection sequence is defined as follows: where x t denotes the given context at position t and I t is the set of inflections corresponding to S t , from which the model should choose y t .


8 The results in Table 5 show that the suggested models outperform the language model substantially for both languages.

the contribution of both bilingual and non-lexical features is noteworthy: adding non-lexical features consistently leads to 1.5% to 2% absolute gain in both monolingual and bilingual settings in both language pairs.


For multiword queries, the system performs a consistency check on possible combinations of words from different translation classes.

it computes the Cartesian product for pairs of translation classes T 1 and T 2 to generate the set P of word pairs, where each word (w 1 and w 2 ) comes from a different translation class: Then the system checks whether each word pair from the set P exists in the database D of discontinuous content word bi-grams which actually cooccur in the TL corpus: The database contains the set of all bi-grams that occur in the corpus with a frequency ≥ 4 within a window of 5 words (over 9M bigrams for each language).


We can interpret these results as an indication that our method, rather than learning individual indirect transformations, models the entire family of transformations entailed by indirect lexical transfer.

it learns a translation strategy which is based on the distributional similarity of words in a monolingual corpus, and applies this strategy to novel, previously unseen examples.


2 Instead, we need to store up popped items in a buffer buf , just as in cube pruning, and enumerate an item only when we are confident that it will never be surpassed in the future.

we would like to have an estimate of the best item not explored yet (analogous to the heuristic function in A* search).


Linearization proceeds in a way that is very close to a pre-order traversal of the tree, except that the relative position of the root node of a subtree is explicitly specified in the order annotation.

to linearize an order-annotated tree, we look into the local order !.u/ annotated at the root node of the tree, and concatenate the linearizations of its constituent parts.


When using a global data-structure to keep track of the constituent blocks, the encoding procedure can be implemented to run in time linear in the number of blocks in the dependency structure.

for projective dependency structures, it still runs in time linear in the number of nodes.


A dependency structure has gap-degree k if and only if the maximal number of components among the annotations !.u/ is k C 1.

a dependency structure is projective iff all of its annotations consist of just one component.


We write LLCFRL.k/ for the class of all dependency languages generated by lexicalized lcfrs with a fan-out of at most k. Proposition 4.

rEGD.k/ D LLCFrL.k C 1/ the class rEGD.0/ of regular dependency languages over projective structures is exactly the class of dependency languages generated by lexicalized context-free grammars.


This paper investigates the relationship between Context-Free Grammar (CFG) parsing and the Eisner/Satta PBDG parsing algorithms, including their extension to second-order PBDG parsing (McDonald, 2006;McDonald and Pereira, 2006).

we show how to use an off-line preprocessing step, the Unfold-Fold transformation, to transform a PBDG into an equivalent CFG that can be parsed in O(n 3 ) time using a version of the CKY algorithm with suitable indexing (Younger, 1967), and extend this transformation so that it captures second-order PBDG dependencies as well.


This grammar requires O(n 3 ) parsing time to parse because the indices are redundant given the constituent's string positions for the reasons described in section 4.

the rightmost terminal of L u is always u l , the leftmost terminal of u R is always u r and the leftmost and rightmost terminals of v M u are v l and u r respectively.


The O(n 3 ) split-head grammar is closely related to the O(n 3 ) PBDG parsing algorithm given by Eisner and Satta (1999).

the steps involved in parsing with this grammar using the CKY algorithm are essentially the same as those performed by the Eisner/Satta algorithm.


MPD is straightforward given the PBDG to CFG reductions described in this paper.

we use the Inside-Outside algorithm to compute the posterior probability of the CFG constituents corresponding to each PBDG dependency, and then use the Viterbi algorithm to find the parse tree that maximizes the sum of these posterior probabilities.


Our reduction to Datalog brings sophisticated techniques for Datalog query evaluation to the problems of parsing and generation, and establishes a tight bound on the computational complexity of recognition for a wide range of grammars.

it shows that the use of higher-order λ-terms for semantic representation need not be avoided for the purpose of achieving computational tractability.


The approach that we describe in this paper is not dependent on the use of PropBank rolesets; any large corpus of semantic role annotations could be generalized in this manner.

our approach would be applicable to corpora with framespecific role labels, e.g.


An important area for future research will be to explore the correlation between our distance metric for syntactic similarity and various quantitative measures of semantic similarity (Pedersen, et al., 2004).

interesting would be to explore whether different senses of a given verb exhibited markedly different profiles of syntactic context.


Given a sentence, the task of Semantic Role Labeling (SRL) consists of analyzing the logical forms expressed by some target verbs or nouns and some constituents of the sentence.

for each predicate (target verb or noun) all the constituents in the sentence which fill semantic arguments (roles) of the predicate have to be recognized.


Many kernels have been proposed and applied to the NLP study.

haussler (1999) proposed the well-known convolution kernels for a discrete structure.


The Grammar-driven Kernel Definition: Given the two improvements discussed above, we can define the new kernel by beginning with the feature vector representation of a parse tree T as follows: where # subtree i (T) is the occurrence number of the i th sub-tree type (subtree i ) in T. Please note that, different from the previous tree kernel, here we loosen the condition for the occurrence of a subtree by allowing both original and reduced rules (Improvement 1) and node feature mutations (Improvement 2).

we modify the criteria by which a subtree is said to occur.


In similarity-based models, on the other hand, two words that have never been seen in the same argument slot in the generalization corpus will have zero similarity.

a similarity-based model can assign a level of preference for an argument r p and word w 0 only if R(w 0 ) ∩ R(Seen(r p )) is nonempty.


This paper presents a case study in the adaptation of a well known task to a language with few NLP resources available.

we deal with SVM based Hebrew NP chunking.


Using anchors improves their method in three aspects: (1) without anchors, similar examples are often indistinguishable to the SVM learner, and in case they have conflicting labels both examples will be given high weights.

both the regular case and the hard case will be considered as hard examples.


Moreover, in English the ratio of unique first words to number of adjacent NPs is 0.068, while in Hebrew it is 0.47.

in Hebrew, almost every second such NP starts with a different word.


Section 3.6 hinted that SVM based chunking might be hurt by using too many lexical features.

the features w −2 ,w +2 were shown to cause the chunker to overfit in English chunking.


We have also evaluated how well the suggestion feature of the annotation tool performs.

we want to determine how useful named entities are in determining the correct bracketing.


The PRED baseline is based on the idea of using the output of the source classifier as a feature in the target classifier.


Chelba and Acero (2004) describe this approach within the context of a maximum entropy classifier, but the idea is more general.

for many learning algorithms (maxent, SVMs, averaged perceptron, naive Bayes, etc.


), one regularizes the weight vector toward zero.

all of these algorithms contain a regularization term on the weights w of the form λ ||w|| 2 2 .


Our formulation is agnostic to the number of "source" domains.

it may be the case that the source data actually falls into a variety of more specific domains.


For the majority of results, MEGAM performed roughly comparably to the best of the systems in the table.

it was not statistically significantly different that AUGMENT on: ACE-NER, CoNLL, PubMed, Treebank-chunk-wsj, Treebank-chunk-swbd3, CNN and Treebank-brown.


We perform this analysis only on the ACE-NER data by looking specifically at the learned weights.

for any given feature f , there will be seven versions of f : one corresponding to the "crossdomain" f and seven corresponding to each domain.


Since we believe that the target instances should in general be given more weight because they better represent the target domain than the source instances, in the second method, we gave the added target instances more weight in the objective func-tion.

we set λ t,u = λ s such that the total contribution of the added target instances is equal to that of all the labeled source instances.


The framework is flexible to support many different strategies for adaptation.

it can support adaptation with some target domain labeled instances as well as that without any labeled target instances.


Here we develop the infinite tree, a new infinite model capable of representing recursive branching structure over an arbitrarily large set of hidden categories.

we develop three infinite tree models, each of which enforces different independence assumptions, and for each model we define a simple direct assignment sampling inference procedure.


The probability of node t being assigned state k is given by: where s(t) denotes the set of siblings of t, f −xt k (x t ) denotes the posterior probability of observation x t given all other observations assigned to state k, and z −t denotes all state assignments except z t .

the probability is proportional to the product of three terms: the probability of the states of t and its siblings given its parent z p(t) , the probability of the states of the children c(t) given z t , and the posterior probability of observation x t given z t .


Our proposed alternative is to calibrate the reference systems against an existing set of human judgments for a range of outputs from different MT systems.

if many of the reference system's outputs are similar to those MT outputs that received low assessments, we conclude this reference system may not be of high quality.


Both issues of combining evidences from heuristic distances and calibrating the quality of pseudo reference systems can be addressed by a probabilistic learning model.

we use regression because its problem formulation fits naturally with the objective of MT evaluations.


We conjecture that a metric that compares inputs against a diverse population of differently imperfect sentences may be more discriminative in judging translation systems than solely comparing against gold standards.

two sentences may be considered equally bad from the perspective of a gold standard, but subtle differences between them may become more prominent if they are compared against sentences in their peer group.


This allows us to have control over the variability of the experiments while answering the questions we posed above (such as the quality of the systems providing the pseudo references, the quality of MT systems being evaluated, and the diversity over the distribution of training examples).

we reserved four systems (MT2, MT5, MT6, and MT9) for the role of pseudo references.


The second measure is the alignment confidence, defined as , where C(a j ) denotes the number of alignments proposed by the word aligner that are identical to a j .

aC(a j ) measures how often the aligner aligns c j and E j when they co-occur.


Table 4 shows the detail of the distribution of alignment types after one and two steps of automatic repacking.

we can observe that the 1: 1 1: 0 1: 1 1: 2 1: 3 1:  Table 4: Distribution of alignment types (%) alignments are more frequent after the application of repacking: the ratio of this type of alignment has increased by 7.81% for Chinese-English and 5.26% for English-Chinese.


However, the counts we give only take into account valid paraphrases of the input.

overgeneration and spurious derivations are excluded from the toll.


This last indicates that GLEU correlates better with human judgements than any of the parsers individually, and is well within the 'moderate' range for correlation interpretation.

for the GLEU-human correlation, the score of 0.4014 is approaching the minimum pairwise human correlation of 0.4710.


Before correlating the human judges' scores and the parser outputs, it is interesting to look at how each parser treats the sentence generation methods, and how this compares with human ratings (Table 6).

note that the Collins parser rates the PoStag-and Supertag-generated sentences more Extracted (Sequence-size 24) After a near three-hour meeting and last-minute talks with President Lennart Meri, the Reform Party council voted overwhelmingly to leave the government.


In future we would like to examine the space of sentence types more fully.

we will look at predicting the fluency of near-human quality sentences.


Previous psycholinguistics studies have shown that the direction of gaze carries information about the focus of the user's attention (Just and Carpenter, 1976).

in human language processing tasks, eye gaze is tightly linked to language production.


Based on the data collected, we investigated the temporal alignment between co-occurred eye gaze and spoken utterances.

we examined the temporal alignment between eye gaze fixations and the corresponding spoken references (i.e., the spoken words that are used to refer to the objects on the graphic display).


Here, the "parallel corpus" refers to a series of speech-gaze pairs, each of them consisting of a list of words from the spoken references in the user utterances and a list of objects that are fixated upon within the same time window.

we first transcribed the user speech into scripts by automatic speech recognition software and then refined them manually.


(i.e., interpretation) evaluation metrics.

for a given object o k the translation model will yield a set of probabilities {Pr(w j |o k ), ∀j}.


It should be stressed that this update does not depend on whether a link was created from x to α.

whatever links the parser assigns, A x (−1) and A x 1 are always updated by the symbols which appear immediately before and after x.


The work we present here extends their work by exploring a k-best version of the MST algo-rithm.

we consider an algorithm proposed by Camerini et al.


While efficient algorithms have been developed for this DOP model by converting it into a PCFG reduction (Goodman 2003), DOP's estimator was shown to be inconsistent by Johnson (2002).

even with unlimited training data, DOP's estimator is not guaranteed to converge to the correct distribution.


In NLP, bagging was used for active learning for text classification (Argamon-Engelson and Dagan, 1999;McCallum and Nigam, 1998).

in parsing, (Henderson and Brill, 2000) applied a constituent level voting scheme to an ensemble of bagged models to increase parser performance, and (Becker and Osborne, 2005) suggested an active learning technique in which the agreement among an ensemble of bagged parsers is used to predict examples valuable for human annotation.


Our approach assumes that if the parameters of the model are well designed to annotate a sentence with a high quality parse, then it is likely that the model will output the same (or a highly similar) parse even if the training data is somewhat changed.

we rely on the stability of the parameters of statistical parsers.


WOODWARD works under several assumptions.

it requires a corpus whose content overlaps at least in part with the content of the parsed sentences.


If we model the buyers' preferences as independently distributed along each dimension and each modifier score a(µ, d k ) also as an independent random variable, then the random variable Π(s i ) is a sum of random variables.

we have: where R(µ j , d k ) is equal to the sum of the r i weights across all postings in which the modifier µ j modifies dimension d k .


After all the control factors are taken into consideration, the modifier scores reflect the additional value of the text opinions.

we used as control variables the product's price on Amazon, the average star rating of the merchant, the number of merchant's past transactions, and the number of sellers for the product.


This means that the binary relation s i s k ("the gloss of synset s i contains a term belonging to synset s k "), which induces a directed graph on the set of WordNet synsets, may be thought of as a channel through which positivity and negativity flow, from the definiendum (the synset s i being defined) to the definiens (a synset s k that contributes to the definition of s i by virtue of its member terms occurring in the gloss of s i ).

if a synset s i is known to be positive (negative), this can be viewed as an indication that the synsets s k to which the terms occurring in the gloss of s i belong, are themselves positive (negative).


In Section 2 we describe a simple structured model that jointly learns and infers sentiment on different levels of granularity.

we reduce the problem of joint sentence and document level analysis to a sequential classification problem using constrained Viterbi inference.


The first uses the Sentence-Structured classifier to classify all the sentences from a review, then passes this information to the document classifier as input.

for every predicate in the original document classifier, an additional predicate that specifies the polarity of the sentence in which this predicate occurred was created.


In both cases there is a slight improvement in performance suggesting that an iterative approach might be beneficial.

a system could start by classifying documents, use the document information to classify sentences, use the sentence information to classify documents, and repeat until convergence.


Another area of future work is to empirically investigate the use of these models on longer documents that require more levels of sentiment analysis than product reviews.

the relative position of a phrase to a contrastive discourse connective or a cue phrase like "in conclusion" or "to summarize" may lead to improved performance since higher level classifications can learn to weigh information passed from these lower level components more heavily.


The idea of using the regularizer of a linear model to encourage the target parameters to be close to the source parameters has been used previously in domain adaptation.

chelba and Acero (2004) showed how this technique can be effective for capitalization adaptation.


Let A be the family of subsets of R k corresponding to characteristic functions of linear classifiers (sets on which a linear classifier returns positive value).

then the A distance between two probability distributions is we find the subset in A on which the distributions differ the most in the L 1 sense.


Our first approach to query expansion treats the questions and answers in the question-answer corpus as two distinct languages.

the 10 million question-answer pairs extracted from FAQ pages are fed as parallel training data into an SMT training pipeline.


We assume the Two-Document Theory 1 , which hypothesizes that the Gospel of Luke and the Gospel of Matthew have as their common sources two documents: the Gospel of Mark, and a lost text customarily denoted Q.

we will consider the Gospel of Luke 2 as the target text, and the Gospel of Mark as the source text.


As shown in Table 6,L test.B is more similar to L test.B than to the hypothesis of other researchers 9 .

when the model is trained on Bovon's text-reuse hypothesis on the train text, its prediction on the test text matches most closely with that of the same researcher, Bovon.


For this boundary position, the 6-gram including the three words before and the three words after the boundary is included as a feature; additionally, all n-grams for n < 6 such that either w i or w i+1 (or both) is in the n-gram are included as features.

all n-grams in a six word window of boundary position i are included as features, except those that include neither w i nor w i+1 in the n-gram.


Against this background, we consider a radically different approach to language modelling: instead of explicitly storing all distinct n-grams, we store a randomised representation.

we show that the Bloom filter (Bloom (1970); BF), a simple space-efficient randomised data structure for representing sets, may be used to represent statistics from larger corpora and for higher-order n-grams to complement a conventional smoothed trigram model within an SMT decoder.


We take advantage of the monotonicity of the ngram event space to place upper bounds on the frequency of an n-gram prior to testing for it in the filter and potentially truncate the outer loop in Algorithm 2 when we know that the test could only return postive in error.

if we have stored lower-order ngrams in the filter, we can infer that an n-gram cannot present, if any of its sub-sequences test negative.


Now the hyper-parameters of the variational Dirichlet posteriors of each node in the Dirichlet-Tree are shared among the Chinese and English model.

we apply only Eqn 3 with fixed e Eq[log θ k ] in the E-step and Eqn 4 in the M-step on {β wk } to bootstrap an English LSA model.


We focus on the non-anaphors that the original classifier fails to identify.

we apply the learned classifier to all the non-anaphors in the training documents.


We can just use this set of NP pairs as the seeds.

an instance i{N P i , N P j } will become a seed pair (E i :E j ) in which N P i corresponds to E i and N P j corresponds to E j .


's method as described in the introduction, 8 with the goal of examining whether SC classification accuracy can be improved by combining the output of individual classifiers in a supervised manner.

we (1) use 80% of the instances generated from the BBN Entity Type Corpus to train the four classifiers; (2) apply the four classifiers and Soon et al.


Constraints are applied during the antecedent selection step.

when employed as a constraint, the Mention KS disallows coreference between two NPs if at least one of them has a Mention value of NO, whereas the SCA KS disallows coreference if the SCA value of the two NPs involved is NO.


Now, we can evaluate the impact of the two KSs on the performance of our baseline resolver.

rows 3-6 of Tables 6 and 7 show the F-measure and the resolution accuracy, respectively, when exactly one of the two KSs is employed by the baseline as either a constraint (C) or a feature (F), and rows 7-10 of the two tables show the results when both KSs are applied to the baseline.


While the coreference results in Table 6 seem to suggest that SCA and mention should be employed as constraints, the resolution results in Table 7 sug-gest that SCA is better encoded as a feature.

(1) in comparison to the baseline, the accuracy of common NP resolution improves by about 5-8% when SCA is encoded as a feature; and (2) whenever SCA is employed as a feature, the overall resolution accuracy is significantly higher for resolvers that use SCs induced by DL and SVM than those that rely on Soon's method for SC determination, with improvements in resolution observed on all three NP types.


Overall, these results provide suggestive evidence that both KSs are useful for learning-based coreference resolution.

mention should be employed as a constraint, whereas SCA should be used as a feature.


The second assumption is similar to HITS if words and sentences are considered as authorities and hubs respectively.

the proposed approach aims to fuse the ideas of PageRank and HITS in a unified framework.


We noticed ASSERT failed to identify relatively many predicates.

it seems predicates such as "is" are sometimes labeled as AUX by the part-of-speech tagger, and subsequently ignored.


Finally, one can also use unlabeled data as part of the supervised training process, which is called transduction or semi-supervised learning.

we find the possibility of using unlabeled data, invariances and the use of transduction exciting.


those in Table 1) and their associated bags as training data, the aim is to induce a relation extraction system that can reliably decide whether two entities mentioned in the same sentence exhibit the target relationship or not.

when tested on the example sentences from Figure 1, the system should classify S 1 , S 3 ,and S 4 as positive, and S 2 and S 5 as negative.


Notice that level signatures subsume level types: the level type of a non-projective edge is the component level of any of possibly several component roots closest to the root of the whole tree.

the level type of an edge is equal to the largest component level occurring in its level signature.


We report significant improvement both when the seed and test data are in the same domain and in the outof-domain adaptation scenario.

we achieve 50% reduction in annotation cost for the in-domain case, yielding an improvement of 66% over previous work, and a 20-33% reduction for the domain adaptation case.


Our results show that self-training is of substantial benefit for the problem.

we present: • 50% reduction in annotation cost when the seed and test data are taken from the same domain, which is 66% higher than any previous result with small manually annotated datasets.


Given a negative value α, we add α to the log-probability of a partial parse tree when the schema application violates the dependency constraints.

when a parse tree violates n dependencies, the log-probability of the parse tree is lowered by nα.


The first combination approach is to keep only dependencies for which there is agreement between the two parsers.

dependencies that are proposed by one parser but not the other are simply discarded.


But unlike that approach, we allow feature values to be ambiguous, and represent each feature as a distribution over (binary) values.

these history features are treated as latent variables.


In this paper we explore the performance of transliteration systems on corpora that are varied in a controlled way.

we control the number, and prior language knowledge of human transliterators used to construct the corpora, and the origin of the source words that make up the corpora.


In this paper, we report our experiments designed to explicitly examine the effect that varying the underlying corpus used in both training and testing systems has on transliteration accuracy.

we vary the number of human transliterators that are used to construct the corpus; and the origin of the English words used in the corpus.


Each of the three sub-corpora can be further divided based on the seven individual transliterators, in different combinations.

construct a subcorpus from T1's transliterations, T2's, and so on; then take all combinations of two transliterators, then three, and so on.


The Dutch collection (D 7 ), shows an unstable trend across transliterators.

accuracy differs in a narrower range for Arabic and English, but in wider range for Dutch.


In this case, backoff only applies for vowels as consonants are supposed to be part of their own independent segments.

if search in the rules of pattern CVC was unsuccessful, it looks for "e" in V pattern.


For example, using CV-MODEL3, the pair (Èô ,merkel) with q S =C and a p2e =((,me),(È,r),(ô,ke),( ,l)), produces just one transformation rule " È ô → merkel" based on a C pattern.

the Persian string contains no vowel characters.


To avoid overfitting the system by long consonant clusters, we perform segmentation based on the English q sequence, but categorise the rules based on their Persian segment counterparts.

for the pair ( È ô ,merkel) with a e2p =((m,),(e,ε),(r,È),(k,ô),(e,ε),(l, )), these rules are generated (with category patterns given in parenthesis): → m (C), È ô → rk (C), → l (C), È ô → merk (C), È ô → rkel (C).


The pair (sutoresu, stress) can be aligned: For the feature representation, we only extract substring pairs that are consistent with this alignment.

1 the letters in our pairs can only be aligned to each other and not to letters outside the pairing: su-s, ut-t, t-t,... es-es, s-s, su-ss...} We define phrase pairs to be the pairs of substrings consistent with the alignment.


Phonetic, semantic, or syntactic features could be included within our discriminative infrastructure to aid in the identification of cognates in text.

we plan to investigate approaches that do not require the bilingual dictionaries or bitexts to generate training data.


The coordination f-score for the oracle trees on section 23 is 83.56%.

: if an 'oracle' were to choose from each set of n-best trees the tree that maximised constituent precision and recall, then the resulting set of oracle trees would have a NP coordination dependency f-score of 83.56%.


Next, it proposes a unified approach to the whole task on the basis of tagging.

it takes the problem as that of assigning tags to the input texts, with a tag representing deletion, preservation, or replacement of a token.


To generate a ranking of U R using the learned HMM parameters, we rank the arguments e i according to how similar their state distributions P (t|e i ) are to those of the seed arguments.

3 we define a function: where KL represents KL divergence, and the outer sum is taken over the arguments e i of the extraction e. We rank the elements of U R in ascending order of f (e).


We replace the deterministic rules in our SG-ITG grammar with probabilistic ones, elevating it to a stochastic grammar.

we develop the three sub models (see Section 3) which influence the choice of production rules for ordering decision.


We have touched only the surface benefits of modeling function words.

our proposal is limited to modeling function words in the source language.


As pointed out in (Al-Onaizan and Papineni, 2006), these strategies make hard decisions in reordering which cannot be undone during decoding.

the choice of reordering is independent from other translation factors, and once a reordering mistake is made, it cannot be corrected by the subsequent decoding.


It is observed in pilot experiments that, for a lot of long sentences containing several clauses, only one of the clauses is reordered.


3What is the quality of the triangulated phrase-table?

we are interested in the resulting distribution and whether it is sufficiently distinct from the standard phrase-table.


If L is a constant function, the inexact computation of the expected F-score runs in linear time and the overall decoding algorithm in quadratic time.

if L = 1 the approximate expected F-score is equal to the F-score of the MAP hypothesis, and the modified inference algorithm reduces to a variant of Viterbi decoding.


Therefore Focus on those summands where y i = y k .

group them into pairs (y, z) where y and z are identical except that y i = 1 and y k = 0, but z i = 0 and z k = 1.


Specifically group them into pairs (y, z) where y and z are identical except that y i = 1 and y k = 0, but z i = 0 and z k = 1.

the two summations on the right-hand side of the following equality are carried out in parallel: Then, focusing on s first: where A = s • z is the number of true positives between s and z (s and y have an additional true positive at i by construction); T = y•y = z•z is the number of positive labels in y and z (identical by assumption); and is the probability of y and z evaluated on all positions except for i and k. This equality holds because of the zeroth-order Markov assumption (5) imposed on Pr(y).


The string z (m) can be transformed into any other string s ∈ S m by repeatedly clearing a more likely set bit and setting a less likely unset bit.

this can be done as follows: First, find the indices where z (m) and s disagree.


Note that h.S L and h.S R refer to spans that are subsequences of p. We recursively compute the score of h as Require: beam width B; Require: weight vector w; 1: Initialize P , the set of accepted spans; 2: Initialize Q, the queue of candidate spans; 3: repeat 4: span p ← argmax p∈Q U (p.S.T.A); Update P with p ; 6: Update Q with p and P ; 7: where U is the score of an action.

the score of an hypothesis is the sum of the score of the most recent action h.A and the scores of the top hypotheses of the context states.


Otherwise, we update the weight vector in the Perceptron style, by promoting the features of the gold standard action, and demoting the features of the action of the top hypothesis.

then we re-generate the queue Q with P and the updated weight vector w. we first remove all the elements in Q, and then generate hypotheses for all the possible spans based on the context spans in P .


This paper explores recent refinements to the nugget-based evaluation methodology developed by NIST.

we examine the recent so-called "pyramid extension" that incorporates relevance judgments from multiple assessors to improve evaluation stability (Lin and Demner-Fushman, 2006).


The purpose of our experiments is to study the impact of the new representations introduced earlier for QA tasks.

we focus on question classification and answer re-ranking for Web-based QA systems.


In the second part of the study, when subjects listened to contextualized versions of the same tokens of okay, most of the strong correlations of word function with acoustic, prosodic and phonetic features were replaced by correlations with contextual features, like latency and turn duration.

these results suggest that contextual features might override the effect of most acoustic, prosodic and phonetic features of okay.


This estimator also minimizes the negative conditional log-likelihood, but uses an L 1 (or Lasso) penalty.

( ) in Equation 2is defined according to = .


These early steps coincide with the Boosting steps most of the time.

the effect of backward steps is more visible at later stages.


The sublanguage I is used during generalization, thus only the most general grammar, , is able to generate the entire sublanguage.

the generalization process is bounded by I , that is why our model is called Grammar Approximation by Representative Sublanguage.


As we go, we will indicate the performance of each model on data from ACE 2004 (NIST, 2004).

we used as our development corpus the English translations of the Arabic and Chinese treebanks, comprising 95 documents and about 3,905 mentions.


We will not assume any other information to be present in the data beyond the text itself.

unlike much related work, we do not assume gold named entity recognition (NER) labels; indeed we do not assume observed NER labels or POS tags at all.


The main challenge in transliteration detection lies in the fact that transliteration is a lossy process.

information can be lost about the original word when it is transliterated.


The major issue in porting the memoriless transducer over to our task of transliteration extraction is that its training is supervised.

it would require a relatively large set of known transliterations for training, and this is exactly what we would like the model to acquire.


Third, how well do learning-based metrics generalize beyond their training examples?

how well can a metric that was developed based on one group of MT systems evaluate the translation qualities of new systems?


Later, Kulesza and Shieber (2004) noted that if a classifier provides a confidence score for its output, that value can be interpreted as a quantitative estimate of the input instance's translation quality.

they trained an SVM classifier that makes its decisions based on a set of input features computed from the sentence to be evaluated; the distance between input feature vector and the separating hyperplane then serves as the evaluation score.


Experimental results confirm that learning from training examples that have been doubly approximated (class labels instead of ordinals, humanlikeness instead of human-acceptability) does negatively impact the performance of the derived metrics.

we showed that they do not generalize as well to new data as metrics trained from direct regression.


Table 3 gives the corresponding clues and patterns.

the returned candidate qualia elements are the lemmatized underlined expressions in PURP:=[VB] NP | NP | be [VBD].


The words with an agreement below 5% are gaze, prisoner, accounting, maturity, complexity and delegation.

our test subjects had substantial difficulties in finding the purpose of such abstract words.


In order to verify that our sample is not biased, we checked that the F-Measure yielded by our 10 randomly selected words (17.7%) does not differ substantially from the overall average F-Measure (17.1%) to be sure that we have chosen words from all F-Measure ranges.

we asked different test subjects which also participated in the creation of the gold standard to rate the qualia elements with respect to their appropriateness for the qualia term using a scale from 0 to 3, whereby 0 means 'wrong', 1 'not totally wrong', 2 'acceptable' and 3 'totally correct'.


Such an approach is especially interesting either for lexicog-raphers aiming at constructing lexicons, but even more for natural language processing systems relying on deep lexical knowledge as represented by qualia structures.

we have focused on learning ranked qualia structures which allow to find an ideal cut-off point to increase the precision/recall trade-off of the learned structures.


We have abstracted from the issue of finding the appropriate cut-off, leaving this for future work.

we have evaluated different ranking measures for this purpose, showing that all of the analyzed measures (Web-P, Web-Jaccard, Web-PMI and the conditional probability) significantly outperformed a baseline using no ranking measure.


With LNRE models we can answer questions such as: If we could obtain as many tokens of metaas we have of re-, would we also see as many distinct types?

is the prefix metaas productive as the prefix re-?


When dealing with descriptions in semiprescriptive sources such as dictionaries, we must always ask whether they accurately represent language use.

is there empirical evidence that speakers fail to use these words?


The second scenario is similar to the first, except that the learning process includes analogical pressure from a random set of words.

for a target concept, the estimated distribution of its IPS is influenced by the distribution of known words.


The grammar can be factored into modular components: In this paper we focus on the probability distribution of concept-inflection pairs.

we focus on the relative frequency of inflectional property sets (IPS) on a lemma-bylemma basis, represented by the middle term above.


As shown in Table 3, as analogical pressure (β) increases, gap life expectancy (τ) decreases.

high analogical pressure quickly eliminates atypical frequency distributions, such as those exhibited by gaps.


Our substring-based transliteration approach is an adaptation of phrase-based models of machine translation to the domain of transliteration.

our methods are inspired by the monotone search algorithm proposed in (Zens and Ney, 2004).


Although the accuracy achieved is less than 100%, the substring-based transducer has the great advantage of being able to handle noise in the input.

if the spelling of an input word does not match an Arabic word from the training data, a lookup table will generate nothing, while the substring-based transducer could still search for the correct transliteration.


In this work, we show that methods developed for SMT can be brought to bear on tasks where logical forms are involved, such as semantic parsing.

we extend the WASP semantic parsing algorithm by adding variable-binding λ-operators to the underlying SCFG.


Rule extraction is forbidden in this case because it would destroy the link between bordering and next to.

the NL and MR parse trees are not isomorphic.


First, algorithms that use Prolog logical forms as the target MRL generally show better recall than those using FunQL.


While the error reduction varies slightly, it turns out that the error reduction is virtually the same for 10% of the training data as for 80%.

there is no correlation if information concerning the lifts are not added to the labels.


In this paper, we investigate methods to automatically generate resources for subjectivity analysis for a new target language by leveraging on the resources and tools available for English, which in many cases took years of work to complete.

through experiments with cross-lingual projection of subjectivity, we seek answers to the following questions.


Second, we validated the two automatically generated subjectivity resources by using them to build a tool for subjectivity analysis in the target language.

we developed two classifiers: a rulebased classifier that relies on the subjectivity lexicon described in Section 3.1, and a machine learning classifier trained on the subjectivity-annotated corpus described in Section 4.1.


Given a collection of sentences, S, the task is to label each sentence as either speculative or nonspeculative (spec or nspec henceforth).

s is to be partitioned into two disjoint sets, one representing sentences that contain some form of hedging, and the other representing those that do not.


In this study, we explore approaches that can segment multiparty conversational speech by integrating various knowledge sources (e.g., words, audio and video recordings, speaker intention and context).

we evaluate the performance of a Maximum Entropy approach, and examine the effectiveness of multimodal features on the task of dialogue segmentation.


As our goal is to identify multimodal cues of segmentation in face-to-face conversation, we use the AMI meeting corpus (Carletta et al., 2006), which includes audiovideo recordings, to test our approach.

we are using 50 scenario-based meetings from the AMI corpus, in which participants are assigned to different roles and given specific tasks related to designing a remote control.


This study explores the use of features from multiple knowledge sources (i.e., words, prosody, motion, interaction cues, speaker intention and role) for developing an automatic segmentation component in spontaneous, multiparty conversational speech.

we addressed the following questions: (1) Can a MaxEnt classifier integrate the potentially characteristic multimodal features for automatic dialogue segmentation?


When calculating the similarity of relations, only the relations with the same type are considered.

the cause-effect (or temporal) relations in a query are only compared with the cause-effect (or temporal) relations in a document.


To deal with the multi-level relevance, the discounted cumulative gain (DCG) (Jarvelin and Kekalainen, 2000) was adopted as the evaluation metric, defined as where i denotes the i-th document in the retrieved list; G[i] denotes the gain value, i.e., relevance levels, of the i-th document, and c denotes the parameter to penalize a retrieved document in a lower rank.

the DCG simultaneously considers the relevance levels, and the ranks in the retrieved list to measure the retrieval precision.


We have improved the performance of social network extraction five-fold, compared to a baseline system that already uses state-of-the-art technology.

we improved the performance of co-reference resolution for conversational speech, by feature engineering and improving the clustering algorithm.


In order to make sure that the process is as language-independent as possible, we declined to make use of any non-English linguistic resources outside of the Wikimedia domain (specifically, Wikipedia and the English language Wiktionary (en.wiktionary.org)).

we did not use any semantic resources such as WordNet or part of speech taggers.


There are a few reasons that redirect pages exist, but the primary purpose is exemplified by the fact that "USA" is an entry which redirects the user to the page entitled "United States."

in the vast majority of cases, redirect pages provide another name for an entity.


As in this paper, and unlike the above mentioned works, Cucerzan made use of the explicit Category information found within Wikipedia.

category and related listderived data were key pieces of information used to differentiate between various meanings of an ambiguous surface form.


Our approach to multilingual NER is to pull back the decision-making process to English whenever possible, so that we could apply some level of linguistic expertise.

by focusing on only one language, we could take maximum advantage of the Category structure, something very difficult to do in the general multilingual case.


Also, we fill in partial lists of names by assuming single nonlower case words between marked names are actually parts of names themselves.

we would replace <ENAMEX TYPE="PERSON">Fred Smith</ENAMEX>, Somename <ENAMEX TYPE="PERSON">Jones </ENAMEX> with <ENAMEX TYPE="PERSON"> Fred Smith</E-NAMEX>, <ENAMEX TYPE= "PERSON"> Somename Jones</ENAMEX>.


An important question remains: "How do these results compare to other methodologies?"

while we can get these results for free, how much work would traditional methods require to achieve comparable results?


Moreover, it seems clear that a Wikipedia-derived system could be used as a supplement to other systems for many more languages.

we have, for all practical purposes, embedded in our system an automatically generated entity dictionary.


As part of this task, coreference resolution systems must decide which pronouns refer to preceding noun phrases (called antecedents) and which do not.

a long-standing challenge has been to correctly classify instances of the English pronoun it.


Müller (2006) summarizes the evolution of computational approaches to nonreferential it detection.

note the pioneering work of Paice and Husk (1987), the inclusion of non-referential it detection in a full anaphora resolution system by Lappin and Leass (1994), and the machine learning approach of Evans (2001).


The 25 classes are passed as input for attribute extraction without modifications.

the instance sets are not manually postfiltered or otherwise changed in any way.


Lemma match For the remaining set of n-grams that are not yet matched, we now relax our matching criteria by allowing a match if their corresponding lemmas match.

a system unigram (l In the case of bigrams, the matching conditions are l s i = l r i and l s i+1 = l r i+1 .


A first attempt at using cohesion to improve SMT output would be to apply our definition as a filter on K-best lists.

we could have a phrasal decoder output a 1000-best list, and return the highestranked cohesive translation to the user.


It is therefore important that our language model is robust with respect to out-of-grammar sentences.

this means that it should provide a reasonable parse tree for any possible word sequence W .


3 Linguistic Resources The linguistic resources presented in this Section are partly influenced by the form of the recognizer output.

the speech recognizer does not always transcribe numbers, compounds and acronyms as single words.


For this reason there is a considerable discrepancy between the final document and what the speaker has said literally.

in medical reports we see differences of the following kinds: • Punctuation marks are typically not verbalized.


Tokens can be ordinary words or special symbols representing line breaks, section headings, etc.

we represent each section heading by a single indivisible token, even if the section name consists of multiple words.


To estimate this model we first align all training documents.

for each document, the target word sequence is segmented into M segments T = τ 1 .


When user specific data is used, the probabilistic approach performs consistently better than TBL on all accounts.

it always has much lower insertion rates reflecting its supe-rior ability to remove utterances that are not typically part of the report.


To overcome this limitation, in (Fang and Zhai, 2006), we proposed a set of semantic term matching constraints and modified the previously derived axiomatic functions to make them satisfy these additional constraints.

the primitive weighting function is generalized as where s(q, d) is a semantic similarity function between two terms q and d, and f is a monotonically increasing function defined as where β is a parameter that regulates the weighting of the original query terms and the semantically similar terms.


We have shown that the proposed generalization can be implemented as a query expansion method.

the expanded terms are selected based on a term similarity function s and the weight of an expanded term t is determined by its term similarity with a query term q, i.e., s(q, t), as well as the weight of the query term, i.e., ω(q).


Furthermore, we examine the query expansion method using co-occurrence-based resources.

we evaluate the retrieval performance using the following two similarity functions: s M IBL and s M IImp .


It is clear that query expansion with these functions can improve the retrieval performance, although the performance gains achieved by different functions vary a lot.


In this paper, we re-examine the problem of query expansion using lexical resources in recently proposed axiomatic framework and find that we are able to significantly improve retrieval performance through query expansion using only hand-crafted lexical resources.

we first study a few term similarity functions exploiting the information from two lexical resources: WordNet and dependency-based thesaurus created by Lin.


Thus, we turn to use the linguistic units carrying on more semantic information.

we make use of two kinds of units: BaseNP (Base Noun Phrase) and WH-ngram.


Instead of dealing with each topic chain individually, the proposed method handles a set of questions together.

given a queried question, we construct a question tree consisting of both the queried question and the related questions, and then apply the MDL principle to select the best cut of the question tree.